Runtime Composition in Dynamic System of Systems: A Systematic Review of Challenges, Solutions, Tools, and Evaluation Methods

TL;DR

This study addresses the problem of runtime composition in dynamic System of Systems (SoSs) by performing a rigorous Systematic Literature Review of 80 primary studies from 2019–2024. It develops a four‑dimensional taxonomy of challenges (modeling/analysis, resilient operations, orchestration, heterogeneity) and a seven‑fold taxonomy of solutions (co-simulation/digital twins, semantics, integration, adaptive architectures, middleware, formal methods, AI-based resilience), complemented by a detailed view of tooling and evaluation methods. Key findings reveal pervasive fragmentation across venues and toolchains, a dominance of simulation-based evaluation, and gaps in benchmarking and socio-technical integration, with Eclipse Arrowhead emerging as a notably mature ecosystem. The authors advocate standardized evaluation metrics, scalable decentralized architectures, and cross-domain, ecosystem-driven approaches to advance runtime-composable SoSs, offering a practical guide for researchers and practitioners toward more interoperable and adaptable systems. This work thus provides a consolidated lens on the state of runtime composition in dynamic SoSs and outlines concrete directions for future research and industry deployment.

Abstract

Context: Modern Systems of Systems (SoSs) increasingly operate in dynamic environments (e.g., smart cities, autonomous vehicles) where runtime composition -- the on-the-fly discovery, integration, and coordination of constituent systems (CSs)--is crucial for adaptability. Despite growing interest, the literature lacks a cohesive synthesis of runtime composition in dynamic SoSs. Objective: This study synthesizes research on runtime composition in dynamic SoSs and identifies core challenges, solution strategies, supporting tools, and evaluation methods. Methods: We conducted a Systematic Literature Review (SLR), screening 1,774 studies published between 2019 and 2024 and selecting 80 primary studies for thematic analysis (TA). Results: Challenges fall into four categories: modeling and analysis, resilient operations, system orchestration, and heterogeneity of CSs. Solutions span seven areas: co-simulation and digital twins, semantic ontologies, integration frameworks, adaptive architectures, middleware, formal methods, and AI-driven resilience. Service-oriented frameworks for composition and integration dominate tooling, while simulation platforms support evaluation. Interoperability across tools, limited cross-toolchain workflows, and the absence of standardized benchmarks remain key gaps. Evaluation approaches include simulation-based, implementation-driven, and human-centered studies, which have been applied in domains such as smart cities, healthcare, defense, and industrial automation. Conclusions: The synthesis reveals tensions, including autonomy versus coordination, the modeling-reality gap, and socio-technical integration. It calls for standardized evaluation metrics, scalable decentralized architectures, and cross-domain frameworks. The analysis aims to guide researchers and practitioners in developing and implementing dynamically composable SoSs.

Figures (4)

• Figure 1: Methodology adopted for the SLR, illustrating its planning, execution, and reporting phases.
• Figure 2: Distribution of publication types for the selected studies.
• Figure 3: Publication trends of the studies selected for the SLR (2019--2024).
• Figure 4: Application domains identified in the selected studies. Unshaded nodes represent domains and subdomains, while shaded nodes indicate case studies.